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Consumption Structure Optimization Strategy for Scenic Spots Using the Deep Learning Model under Digital Economy.

Yi Wang1, Na Li2, Xiaoe Qu1

  • 1School of Economics and Finance, Xi'an Jiaotong University, Xi'an 710000, China.

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|August 15, 2022
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Summary

This study analyzed the economic development of OCT East using the backpropagation neural network (BPNN) model. Results show steady growth, with room for improvement in its tourism consumption structure.

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Area of Science:

  • Tourism Economics
  • Applied Artificial Intelligence
  • Regional Economic Development

Background:

  • The consumption economy structure of scenic spots faces challenges impacting rational development.
  • Evaluating the economic development level of tourism complexes requires robust analytical models.

Purpose of the Study:

  • To identify problems in scenic spot consumption economy structures.
  • To promote rationalization of scenic spot consumption economies.
  • To analyze the economic development level of Overseas Chinese Town East (OCT East) using a backpropagation neural network (BPNN) model.

Main Methods:

  • Analytic Hierarchy Process (AHP) to determine index weights and obtain comprehensive evaluation values.
  • Backpropagation Neural Network (BPNN) model for training, testing, and evaluating economic development levels.
  • Analysis of Tourism Consumption Structure (TCS) to expand consumption economic structure research methods.

Main Results:

  • OCT East's development level index grew steadily from 0.2457 in 2012 to 0.5304 in 2021.
  • The development level reached a
  • high-level
  • in 2019 (0.6497), though the average index (0.5662) indicates potential for further growth.
  • Development stages identified: low in 2012, high from 2013-2018, and high from 2019-2021.

Conclusions:

  • The BPNN model effectively evaluates the economic development level of tourism complexes like OCT East.
  • Targeted optimization strategies can address existing problems and enhance the consumption economy of scenic spots.
  • This research contributes to optimizing scenic spot consumption and advancing the social tourism economy.